• [email protected]
  • +971 507 888 742
Submit Manuscript
SciAlert
  • Home
  • Journals
  • Information
    • For Authors
    • For Referees
    • For Librarian
    • For Societies
  • Contact
  1. Information Technology Journal
  2. Vol 11 (10), 2012
  3. 1496-1501
  • Online First
  • Current Issue
  • Previous Issues
  • More Information
    Aims and Scope Editorial Board Guide to Authors Article Processing Charges
    Submit a Manuscript

Information Technology Journal

Year: 2012 | Volume: 11 | Issue: 10 | Page No.: 1496-1501
DOI: 10.3923/itj.2012.1496.1501

Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

Article Trend



Total views 181

Authors


Fengyu Zhu

Country: China

Qi Wang

Country: China

Zhengguang Shen

Country: China

Keywords


  • fault detection
  • particle swarm optimization
  • Relevance vector machine
  • liquid rocket engines
Research Article

APSO-RVM for Fault Detection of Liquid Rocket Engines Test-bed

Fengyu Zhu, Qi Wang and Zhengguang Shen
Selection of Relevance Vector Machine (RVM) kernel function parameter is one among ineffectively resolved issues which is first resolved in the literature by Adaptive Particle Swarm Optimization (APSO). A novel APSO-RVM method is proposed to optimize and select the RVM kernel parameter, thus forming, taking the advantage of APSO dramatically convergence. Furthermore, the method is applied to the fault detection of liquid rocket engines test-bed. In order to verify the validity of dramatically effectiveness in fault detection, this paper demonstrates the proposed APSO-RVM approach by performing both simulations and experiments using Oxygen Valve Outlet Pressure (Pejy) data. Results show that APSO-RVM can rapidly detect faults effectively and has a high practical value.
PDF Fulltext XML References Citation

How to cite this article

Fengyu Zhu, Qi Wang and Zhengguang Shen, 2012. APSO-RVM for Fault Detection of Liquid Rocket Engines Test-bed. Information Technology Journal, 11: 1496-1501.

DOI: 10.3923/itj.2012.1496.1501

URL: https://scialert.net/abstract/?doi=itj.2012.1496.1501

Related Articles

Segmented Tracks Planning of Roadway-Powered System for Electric Vehicles using Improved Particle Swarm Optimization
An Improved PSO Algorithm Coupling with Prior Information for Function Approximation
Missile Fault Detection Based on Linear Parameter Varying Fault Detection Filter

Leave a Comment


Your email address will not be published. Required fields are marked *

Useful Links

  • Journals
  • For Authors
  • For Referees
  • For Librarian
  • For Socities

Contact Us

Office Number 1128,
Tamani Arts Building,
Business Bay,
Deira, Dubai, UAE

Phone: +971 507 888 742
Email: [email protected]

About Science Alert

Science Alert is a technology platform and service provider for scholarly publishers, helping them to publish and distribute their content online. We provide a range of services, including hosting, design, and digital marketing, as well as analytics and other tools to help publishers understand their audience and optimize their content. Science Alert works with a wide variety of publishers, including academic societies, universities, and commercial publishers.

Follow Us
© Copyright Science Alert. All Rights Reserved